人脸生成(Face Generation)

在该项目中,你将使用生成式对抗网络(Generative Adversarial Nets)来生成新的人脸图像。

获取数据

该项目将使用以下数据集:

  • MNIST
  • CelebA

由于 CelebA 数据集比较复杂,而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型,以让你更快的评估所建立模型的性能。

如果你在使用 FloydHub, 请将 data_dir 设置为 "/input" 并使用 FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [5]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

探索数据(Explore the Data)

MNIST

MNIST 是一个手写数字的图像数据集。你可以更改 show_n_images 探索此数据集。

In [6]:
show_n_images = 64

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[6]:
<matplotlib.image.AxesImage at 0x105754908>

CelebA

CelebFaces Attributes Dataset (CelebA) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸,不会用不到相关说明。你可以更改 show_n_images 探索此数据集。

In [7]:
show_n_images = 50

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[7]:
<matplotlib.image.AxesImage at 0x10639d550>

预处理数据(Preprocess the Data)

由于该项目的重点是建立 GANs 模型,我们将为你预处理数据。

经过数据预处理,MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分,然后调整到 28x28 维度。

MNIST 数据集中的图像是单通道的黑白图像,CelebA 数据集中的图像是 三通道的 RGB 彩色图像

建立神经网络(Build the Neural Network)

你将通过部署以下函数来建立 GANs 的主要组成部分:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

检查 TensorFlow 版本并获取 GPU 型号

检查你是否使用正确的 TensorFlow 版本,并获取 GPU 型号

In [9]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.5.0
/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel_launcher.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.
  

输入(Input)

部署 model_inputs 函数以创建用于神经网络的 占位符 (TF Placeholders)。请创建以下占位符:

  • 输入图像占位符: 使用 image_widthimage_heightimage_channels 设置为 rank 4。
  • 输入 Z 占位符: 设置为 rank 2,并命名为 z_dim
  • 学习速率占位符: 设置为 rank 0。

返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。

In [10]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

辨别器(Discriminator)

部署 discriminator 函数创建辨别器神经网络以辨别 images。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。

该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。

In [11]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
#     print("images = {}".format(images))
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)

        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

生成器(Generator)

部署 generator 函数以使用 z 生成图像。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "generator" 的变量空间名来重复使用该函数中的变量。

该函数应返回所生成的 28 x 28 x out_channel_dim 维度图像。

In [12]:
def generator(z, out_channel_dim,is_train=True,  alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
#     print("z={} ".format(z))
    # TODO: Implement Function
    with tf.variable_scope("generator", reuse= not is_train):
#     with tf.variable_scope('generator', reuse=not):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)

        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')
        # 28x28x5 now
        
#         print("logits = {}".format(logits))
        out = tf.tanh(logits)
#         print("out = {}".format(out))
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

损失函数(Loss)

部署 model_loss 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。

使用你已实现的函数:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [13]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

优化(Optimization)

部署 model_opt 函数实现对 GANs 的优化。使用 tf.trainable_variables 获取可训练的所有变量。通过变量空间名 discriminatorgenerator 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。

In [14]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

训练神经网络(Neural Network Training)

输出显示

使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出,这会帮你评估 GANs 模型的训练程度。

In [16]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

训练

部署 train 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

使用 show_generator_output 函数显示 generator 在训练过程中的输出。

注意:在每个批次 (batch) 中运行 show_generator_output 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 generator 的输出。

In [18]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension #噪音向量长度
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    print("--------------train-----------------")
    # TODO: Build Model
    _, img_width, img_height, img_channels = data_shape  #(60000, 28, 28, 1) “_”,对应6000 无意义
    input_real, input_z, lr = model_inputs(img_width, img_height, img_channels, z_dim)  
    d_loss, g_loss = model_loss(input_real, input_z, img_channels)  
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)  
    steps = 0  
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1  
                
                #老师批语,保留复习使用
                #这里由于 generator 的输出应用了tanh,tanh函数输出在-1到1之间,但是batch_images的范围在-0.5到0.5之间,
                #所以这个地方需要将real image的范围rescale到-1到1之间,这里可以通过batch_images = batch_images*2来实现
                #这样给discriminator传入的real image 和generator的fake image就在相同的范围了。这也是你的图片生成发灰的原因
                
                batch_images = batch_images*2
                  
                # Sample random noise for G  
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))  
                  
                # Run optimizers  
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z,lr:learning_rate})  
                _ = sess.run(g_opt, feed_dict={input_real: batch_images,input_z: batch_z,lr:learning_rate })  
                if steps % 100 == 0:  
                    # At the end of each epoch, get the losses and print them out  
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})  
                    train_loss_g = g_loss.eval({input_z: batch_z})  
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),  
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),  
                          "Generator Loss: {:.4f}".format(train_loss_g))  
                    show_generator_output(sess, 100, input_z, img_channels, data_image_mode)
                
                

MNIST

在 MNIST 上测试你的 GANs 模型。经过 2 次迭代,GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失,或接近 0。

老师批语,保留,复习使用 基本上你的参数设置的都还比较合理。 注意我们的参数最好设置成2的倍数,比如4、8、16、32、64。这样可以让tensorflow在计算的时候进行优化,让你的模型训练更加迅速。Batch size 主要影响的是你GAN生成的图片质量,下面给你一些关于参数设置的建议:

对于celeA这个数据集来说,由于它包含了许多大图像,所以Batch size设置为16或者32比较合适。 对于MNIST这个数据集来说,图像相对较小,只是28 * 28 的黑白色图形,所以Batch size 设置为32 或者64都是可以的。 在GAN中,learning rate 设置为0.0002应该不错,但是有些稍微提高一点能够有效地减少你训练的时间(0.001左右)。 Beta 在0.5或0.4左右的话,也不错。 你可以尝试着调整一下这些参数,应该能得到不错的效果。 z dim一般设置为100比较好~

In [19]:
#注意我们的参数最好设置成2的倍数,比如4、8、16、32、64
#根据老师批语,图像大的话适当小一些,图像大的话,适当小一些。图像小的话适当大一些
#Batch size 主要影响的是你GAN生成的图片质量,下面给你一些关于参数设置的建议
batch_size = 32 
z_dim = 100 #通DCGAN中的 z_size = 100 ,噪音向量的长度,用于在生成器中生成图片
learning_rate = 0.001
#beta1: The exponential decay rate for the 1st moment in the optimizer
#β1:在优化的第一个时刻的指数衰减率
beta1 = 0.4 


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))

# print(mnist_dataset.get_batches)
# print(mnist_dataset.shape)
# print(mnist_dataset.image_mode)


with tf.Graph().as_default():
    print("--------------------------------------")
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
    print("-------------------------------------")
--------------------------------------
--------------train-----------------
Epoch 1/2... Discriminator Loss: 1.6413... Generator Loss: 0.5034
Epoch 1/2... Discriminator Loss: 1.1502... Generator Loss: 0.9527
Epoch 1/2... Discriminator Loss: 2.0040... Generator Loss: 0.1892
Epoch 1/2... Discriminator Loss: 1.3570... Generator Loss: 1.2423
Epoch 1/2... Discriminator Loss: 1.3286... Generator Loss: 0.4727
Epoch 1/2... Discriminator Loss: 1.6341... Generator Loss: 0.3111
Epoch 1/2... Discriminator Loss: 1.7332... Generator Loss: 0.2372
Epoch 1/2... Discriminator Loss: 1.4505... Generator Loss: 0.3950
Epoch 1/2... Discriminator Loss: 1.1208... Generator Loss: 0.6181
Epoch 1/2... Discriminator Loss: 1.1301... Generator Loss: 0.6422
Epoch 1/2... Discriminator Loss: 1.6290... Generator Loss: 0.4696
Epoch 1/2... Discriminator Loss: 3.6329... Generator Loss: 0.0584
Epoch 1/2... Discriminator Loss: 0.8235... Generator Loss: 0.7917
Epoch 1/2... Discriminator Loss: 1.6820... Generator Loss: 0.3127
Epoch 1/2... Discriminator Loss: 0.7735... Generator Loss: 0.9757
Epoch 1/2... Discriminator Loss: 0.9328... Generator Loss: 0.8140
Epoch 1/2... Discriminator Loss: 0.6433... Generator Loss: 1.3316
Epoch 1/2... Discriminator Loss: 0.3547... Generator Loss: 1.9649
Epoch 2/2... Discriminator Loss: 0.7501... Generator Loss: 1.1080
Epoch 2/2... Discriminator Loss: 1.5103... Generator Loss: 0.3584
Epoch 2/2... Discriminator Loss: 0.3976... Generator Loss: 1.5575
Epoch 2/2... Discriminator Loss: 0.6155... Generator Loss: 1.2425
Epoch 2/2... Discriminator Loss: 0.7744... Generator Loss: 0.9099
Epoch 2/2... Discriminator Loss: 0.9651... Generator Loss: 0.6902
Epoch 2/2... Discriminator Loss: 0.5522... Generator Loss: 1.1466
Epoch 2/2... Discriminator Loss: 0.6690... Generator Loss: 1.2637
Epoch 2/2... Discriminator Loss: 0.2943... Generator Loss: 2.2023
Epoch 2/2... Discriminator Loss: 0.9724... Generator Loss: 0.8172
Epoch 2/2... Discriminator Loss: 0.4125... Generator Loss: 1.5490
Epoch 2/2... Discriminator Loss: 0.5088... Generator Loss: 1.7082
Epoch 2/2... Discriminator Loss: 0.1467... Generator Loss: 2.9123
Epoch 2/2... Discriminator Loss: 0.2732... Generator Loss: 2.3512
Epoch 2/2... Discriminator Loss: 0.5799... Generator Loss: 1.6526
Epoch 2/2... Discriminator Loss: 0.9468... Generator Loss: 0.9191
Epoch 2/2... Discriminator Loss: 1.7162... Generator Loss: 6.2968
Epoch 2/2... Discriminator Loss: 0.6115... Generator Loss: 1.1105
Epoch 2/2... Discriminator Loss: 0.6240... Generator Loss: 1.1737
-------------------------------------

CelebA

在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代,或者当 GANs 开始产生真实人脸图像时停止它。

In [20]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1


celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))

print(mnist_dataset.get_batches)
print(mnist_dataset.shape)
print(mnist_dataset.image_mode)
print("--------------------------------------")
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
<bound method Dataset.get_batches of <helper.Dataset object at 0x181483fe48>>
(60000, 28, 28, 1)
L
--------------------------------------
--------------train-----------------
Epoch 1/1... Discriminator Loss: 2.6050... Generator Loss: 0.0980
Epoch 1/1... Discriminator Loss: 0.4874... Generator Loss: 1.3691
Epoch 1/1... Discriminator Loss: 0.8435... Generator Loss: 1.7263
Epoch 1/1... Discriminator Loss: 0.1929... Generator Loss: 2.1371
Epoch 1/1... Discriminator Loss: 0.2311... Generator Loss: 2.9208
Epoch 1/1... Discriminator Loss: 1.8587... Generator Loss: 2.2919
Epoch 1/1... Discriminator Loss: 0.7869... Generator Loss: 1.1016
Epoch 1/1... Discriminator Loss: 1.1777... Generator Loss: 0.8641
Epoch 1/1... Discriminator Loss: 2.0720... Generator Loss: 0.1779
Epoch 1/1... Discriminator Loss: 1.2980... Generator Loss: 0.5143
Epoch 1/1... Discriminator Loss: 3.8229... Generator Loss: 3.8840
Epoch 1/1... Discriminator Loss: 0.4817... Generator Loss: 1.0999
Epoch 1/1... Discriminator Loss: 0.3234... Generator Loss: 2.2032
Epoch 1/1... Discriminator Loss: 0.8343... Generator Loss: 1.6273
Epoch 1/1... Discriminator Loss: 1.3488... Generator Loss: 0.8672
Epoch 1/1... Discriminator Loss: 0.9626... Generator Loss: 3.2687
Epoch 1/1... Discriminator Loss: 0.3635... Generator Loss: 1.4341
Epoch 1/1... Discriminator Loss: 0.7204... Generator Loss: 1.0465
Epoch 1/1... Discriminator Loss: 0.3670... Generator Loss: 1.5105
Epoch 1/1... Discriminator Loss: 0.1713... Generator Loss: 4.6773
Epoch 1/1... Discriminator Loss: 0.0449... Generator Loss: 3.8951
Epoch 1/1... Discriminator Loss: 1.2303... Generator Loss: 0.5709
Epoch 1/1... Discriminator Loss: 1.2306... Generator Loss: 0.5368
Epoch 1/1... Discriminator Loss: 1.5046... Generator Loss: 0.3777
Epoch 1/1... Discriminator Loss: 0.1043... Generator Loss: 5.1160
Epoch 1/1... Discriminator Loss: 1.9664... Generator Loss: 3.1350
Epoch 1/1... Discriminator Loss: 0.3021... Generator Loss: 2.0867
Epoch 1/1... Discriminator Loss: 1.0425... Generator Loss: 1.4843
Epoch 1/1... Discriminator Loss: 0.7495... Generator Loss: 1.0218
Epoch 1/1... Discriminator Loss: 1.6226... Generator Loss: 0.2965
Epoch 1/1... Discriminator Loss: 0.9832... Generator Loss: 1.1655

提交项目

提交本项目前,确保运行所有 cells 后保存该文件。

保存该文件为 "dlnd_face_generation.ipynb", 并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。